import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
				from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     Any=1_3     ,  snake_case__     :     Optional[int]=6_4     ,  snake_case__     :     Dict=2     ,  snake_case__     :     int=3     ,  snake_case__     :     int=True     ,  snake_case__     :     List[Any]=True     ,  snake_case__     :     Optional[int]=3_2     ,  snake_case__     :     str=5     ,  snake_case__     :     List[Any]=4     ,  snake_case__     :     Optional[int]=3_7     ,  snake_case__     :     List[Any]="gelu"     ,  snake_case__     :     str=0.1     ,  snake_case__     :     Union[str, Any]=0.1     ,  snake_case__     :     Union[str, Any]=1_0     ,  snake_case__     :     str=0.02     ,  snake_case__     :     str=[1, 1_6, 4, 4]     ,  snake_case__     :     Tuple=None     ,  )      ->	Tuple:
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  image_size
				_lowerCamelCase				   =  patch_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  is_training
				_lowerCamelCase				   =  use_labels
				_lowerCamelCase				   =  hidden_size
				_lowerCamelCase				   =  num_hidden_layers
				_lowerCamelCase				   =  num_attention_heads
				_lowerCamelCase				   =  intermediate_size
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  hidden_dropout_prob
				_lowerCamelCase				   =  attention_probs_dropout_prob
				_lowerCamelCase				   =  type_sequence_label_size
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  scope
				_lowerCamelCase				   =  backbone_featmap_shape

				# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				# the number of patches is based on the feature map of the backbone, which by default uses an output stride
				# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
				_lowerCamelCase				   =  (self.image_size // 3_2) ** 2
				_lowerCamelCase				   =  num_patches + 1
		def   _snake_case       (       self     :     int		)      ->	List[str]:
				_lowerCamelCase				   =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				_lowerCamelCase				   =  None
				if self.use_labels:
						_lowerCamelCase				   =  ids_tensor([self.batch_size]     ,  self.type_sequence_label_size		)

				_lowerCamelCase				   =  self.get_config()

				return config, pixel_values, labels
		def   _snake_case       (       self     :     List[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  {
				    'global_padding': 'same',
				    'layer_type': 'bottleneck',
				    'depths': [3, 4, 9],
				    'out_features': ['stage1', 'stage2', 'stage3'],
				    'embedding_dynamic_padding': True,
				    'hidden_sizes': [4, 8, 1_6, 3_2],
				    'num_groups': 2,
				}

				return ViTHybridConfig(
				    image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  is_decoder=snake_case__     ,  initializer_range=self.initializer_range     ,  backbone_featmap_shape=self.backbone_featmap_shape     ,  backbone_config=snake_case__     ,  )
		def   _snake_case       (       self     :     str     ,  snake_case__     :     str     ,  snake_case__     :     Tuple     ,  snake_case__     :     Tuple		)      ->	int:
				_lowerCamelCase				   =  ViTHybridModel(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)		)
		def   _snake_case       (       self     :     int     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     List[str]		)      ->	List[Any]:
				_lowerCamelCase				   =  self.type_sequence_label_size
				_lowerCamelCase				   =  ViTHybridForImageClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__     ,  labels=snake_case__		)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)		)



		def   _snake_case       (       self     :     str		)      ->	int:
				_lowerCamelCase				   =  self.prepare_config_and_inputs()
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  config_and_inputs
				_lowerCamelCase				   =  {'pixel_values': pixel_values}
				return config, inputs_dict





@require_torch
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowerCAmelCase_     =   (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		def   _snake_case       (       self     :     Optional[int]		)      ->	Any:
				_lowerCamelCase				   =  ViTHybridModelTester(self		)
				_lowerCamelCase				   =  ConfigTester(self     ,  config_class=snake_case__     ,  has_text_modality=snake_case__     ,  hidden_size=3_7		)
		def   _snake_case       (       self     :     Tuple		)      ->	List[str]:
				self.config_tester.run_common_tests()
		@unittest.skip(reason='ViT does not use inputs_embeds'		)
		def   _snake_case       (       self     :     str		)      ->	Optional[int]:
				pass
		def   _snake_case       (       self     :     Tuple		)      ->	Dict:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)		)
						_lowerCamelCase				   =  model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(snake_case__     ,  nn.Linear		)		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	Dict:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						_lowerCamelCase				   =  inspect.signature(model.forward		)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowerCamelCase				   =  [*signature.parameters.keys()]

						_lowerCamelCase				   =  ['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  snake_case__		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	List[str]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     List[str]		)      ->	Tuple:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*snake_case__		)
		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				_lowerCamelCase				   =  _config_zero_init(snake_case__		)
				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(config=snake_case__		)
						# Skip the check for the backbone
						for name, module in model.named_modules():
								if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
										_lowerCamelCase				   =  [f"""{name}.{key}""" for key in module.state_dict().keys()]
										break

						for name, param in model.named_parameters():
								if param.requires_grad:
										if name in backbone_params:
												continue
										self.assertIn(
										    ((param.data.mean() * 1e9).round() / 1e9).item()     ,  [0.0, 1.0]     ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     ,  )



		@slow
		def   _snake_case       (       self     :     List[Any]		)      ->	Union[str, Any]:
				for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowerCamelCase				   =  ViTHybridModel.from_pretrained(snake_case__		)
						self.assertIsNotNone(snake_case__		)



def 	lowerCamelCase					(	)   ->    Tuple:
		_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
		return image





@require_torch
@require_vision
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@cached_property
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Any:
				return (
				    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
				    if is_vision_available()
				    else None
				)
		@slow
		def   _snake_case       (       self     :     Optional[int]		)      ->	Tuple:
				_lowerCamelCase				   =  ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
				    snake_case__		)

				_lowerCamelCase				   =  self.default_image_processor
				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([-1.9090, -0.4993, -0.2389]		).to(snake_case__		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  snake_case__     ,  atol=1e-4		)		)



		@slow
		@require_accelerate
		def   _snake_case       (       self     :     str		)      ->	str:
				_lowerCamelCase				   =  ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'		)
				_lowerCamelCase				   =  ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'     ,  device_map='auto'		)

				_lowerCamelCase				   =  prepare_img()

				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		)
				_lowerCamelCase				   =  model(**snake_case__		)
				_lowerCamelCase				   =  outputs.logits
				# model predicts one of the 1000 ImageNet classes
				_lowerCamelCase				   =  logits.argmax(-1		).item()

				self.assertTrue(model.config.idalabel[predicted_class_idx]     ,  'tabby, tabby cat'		)
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['pixel_values']
		def __init__(       self     :     List[str]     ,  snake_case__     :     bool = True     ,  snake_case__     :     Dict[str, int] = None     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     bool = True     ,  snake_case__     :     Union[int, float] = 1 / 2_5_5     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     bool = True     ,  **snake_case__     :     List[Any]     ,  )      ->	None:
				super().__init__(**snake_case__		)
				_lowerCamelCase				   =  size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  do_resize
				_lowerCamelCase				   =  size
				_lowerCamelCase				   =  resample
				_lowerCamelCase				   =  do_rescale
				_lowerCamelCase				   =  rescale_factor
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				_lowerCamelCase				   =  image_std if image_std is not None else OPENAI_CLIP_STD
				_lowerCamelCase				   =  do_convert_rgb
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Dict[str, int]     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Dict     ,  )      ->	np.ndarray:
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				_lowerCamelCase				   =  (size['height'], size['width'])
				return resize(snake_case__     ,  size=snake_case__     ,  resample=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[int, float]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	int:
				return rescale(snake_case__     ,  scale=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	np.ndarray:
				return normalize(snake_case__     ,  mean=snake_case__     ,  std=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     ImageInput     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Dict[str, int]] = None     ,  snake_case__     :     PILImageResampling = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[float] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     bool = None     ,  snake_case__     :     ChannelDimension = ChannelDimension.FIRST     ,  **snake_case__     :     str     ,  )      ->	PIL.Image.Image:
				_lowerCamelCase				   =  do_resize if do_resize is not None else self.do_resize
				_lowerCamelCase				   =  resample if resample is not None else self.resample
				_lowerCamelCase				   =  do_rescale if do_rescale is not None else self.do_rescale
				_lowerCamelCase				   =  rescale_factor if rescale_factor is not None else self.rescale_factor
				_lowerCamelCase				   =  do_normalize if do_normalize is not None else self.do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else self.image_mean
				_lowerCamelCase				   =  image_std if image_std is not None else self.image_std
				_lowerCamelCase				   =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				_lowerCamelCase				   =  size if size is not None else self.size
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  make_list_of_images(snake_case__		)

				if not valid_images(snake_case__		):
						raise ValueError(
						    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
						    'torch.Tensor, tf.Tensor or jax.ndarray.'		)

				if do_resize and size is None or resample is None:
						raise ValueError('Size and resample must be specified if do_resize is True.'		)

				if do_rescale and rescale_factor is None:
						raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						_lowerCamelCase				   =  [convert_to_rgb(snake_case__		) for image in images]

				# All transformations expect numpy arrays.
				_lowerCamelCase				   =  [to_numpy_array(snake_case__		) for image in images]

				if do_resize:
						_lowerCamelCase				   =  [self.resize(image=snake_case__     ,  size=snake_case__     ,  resample=snake_case__		) for image in images]

				if do_rescale:
						_lowerCamelCase				   =  [self.rescale(image=snake_case__     ,  scale=snake_case__		) for image in images]

				if do_normalize:
						_lowerCamelCase				   =  [self.normalize(image=snake_case__     ,  mean=snake_case__     ,  std=snake_case__		) for image in images]

				_lowerCamelCase				   =  [to_channel_dimension_format(snake_case__     ,  snake_case__		) for image in images]

				_lowerCamelCase				   =  BatchFeature(data={'pixel_values': images}     ,  tensor_type=snake_case__		)

				return encoded_outputs